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New model from https://wandb.ai/wandb/huggingtweets/runs/3tc6nf11
a7d7fc9
metadata
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
  - huggingtweets
widget:
  - text: My dream is
🤖 AI CYBORG 🤖
World Economic Forum & emma & Shell Nigeria
@__emmamme__-shell_nigeria-wef

I was made with huggingtweets.

Create your own bot based on your favorite user with the demo!

How does it work?

The model uses the following pipeline.

pipeline

To understand how the model was developed, check the W&B report.

Training data

The model was trained on tweets from World Economic Forum & emma & Shell Nigeria.

Data World Economic Forum emma Shell Nigeria
Tweets downloaded 3250 151 3195
Retweets 29 6 455
Short tweets 6 29 13
Tweets kept 3215 116 2727

Explore the data, which is tracked with W&B artifacts at every step of the pipeline.

Training procedure

The model is based on a pre-trained GPT-2 which is fine-tuned on @emmamme-shell_nigeria-wef's tweets.

Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.

At the end of training, the final model is logged and versioned.

How to use

You can use this model directly with a pipeline for text generation:

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/__emmamme__-shell_nigeria-wef')
generator("My dream is", num_return_sequences=5)

Limitations and bias

The model suffers from the same limitations and bias as GPT-2.

In addition, the data present in the user's tweets further affects the text generated by the model.

About

Built by Boris Dayma

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For more details, visit the project repository.

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